Cost Volume Formulation. We first compute two-view matching costs and then aggregate them into a single cost. The two-view matching cost is computed per sampled depth hypothesis to form cost volume [1]}, [2]}, [3]}. Given a sampled depth hypothesis \(d_j (j=1…D)\)  along with all camera parameters, we warp the feature maps of source image \(\lbrace F_i\rbrace _{i=1}^N\)  into this depth plane. Let \(F_i^w (d_j)\)  be the warped feature map of source image \(i\)  at the depth hypothesis \(d_j\) . The two-view matching cost \(V_i (d_j)\)  between the reference image \(I_0\)  and source image \(I_i\)  at the depth hypothesis \(d_j\)  is determined as \(V_i (d_j ) = \left\langle F_{0,i} ,F_i^w (d_j) \right\rangle \) , where \(\langle .,. \rangle \)  is the pixel-wise inner product for measuring the similarity of two feature maps.
